Digital watermarking with PCA based reference images

Principal Components Analysis (PCA) is a valuable technique for dimensionality reduction purposes for huge datasets. Principal components are linear combination of the original variables. The projection of data on this linear subspace keeps the most of the original characteristics. This helps to find robust characteristics for watermarking applications. Most of the PCA based watermarking methods were done in projection space i.e. in eigen image. In this study, different from the other methods, PCA is used to obtain a reference of the cover image by using compression property of PCA. PCA and block-PCA based methods are proposed by using some of the principal vectors in reconstruction. The watermarking is done according to difference of the original and its reference image. The method is compared with Discrete Wavelet Transform (DWT) based approach and its performance against some attacks is discussed.